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Respiratory Aerosol Emissions from Vocalization: Age and Sex Differences Are Explained by Volume and Exhaled CO2

  • Nicholas Good
    Nicholas Good
    Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
  • Kristen M. Fedak
    Kristen M. Fedak
    Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
  • Dan Goble
    Dan Goble
    School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United States
    More by Dan Goble
  • Amy Keisling
    Amy Keisling
    School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United States
    Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
    More by Amy Keisling
  • Christian L’Orange
    Christian L’Orange
    Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
  • Emily Morton
    Emily Morton
    School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United States
    More by Emily Morton
  • Rebecca Phillips
    Rebecca Phillips
    School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United States
  • Ky Tanner
    Ky Tanner
    Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
    More by Ky Tanner
  • , and 
  • John Volckens*
    John Volckens
    Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
    Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
    *Email: [email protected]. Phone: 970-491-6341. Corresponding author address: Department of Mechanical Engineering, Campus Delivery 1374, Colorado State University, Fort Collins, CO, 80521 USA.
Cite this: Environ. Sci. Technol. Lett. 2021, 8, 12, 1071–1076
Publication Date (Web):November 9, 2021
https://doi.org/10.1021/acs.estlett.1c00760

Copyright © 2021 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY-NC-ND 4.0.
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Supporting Info (1)»

Abstract

Evidence suggests that airborne transmission of infectious respiratory aerosol plays an important role for the SARS-CoV-2 virus. This work characterized respiratory aerosol emissions from a panel of healthy individuals of varying age and sex while talking and singing in a controlled laboratory setting. Particle number concentrations between 0.25 and 33 μm were measured from 63 participants aged 12–61 years with concurrent monitoring of voice volume and exhaled CO2 levels. On average, singing produced 77% (95% CI: 42,109%) more aerosol than talking, adults produced 62% (CI: 27,98%) more aerosol than minors, and males produced 34% (CI: 0,70%) more aerosol than females. After accounting for participant voice volume and exhaled CO2 (both of which were positively correlated with aerosol emissions) in linear models, the age and sex differences were attenuated and no longer statistically significant. These results support further investigation of voice volume and CO2 as indicators of infection risk indoors.

This publication is licensed under

CC-BY-NC-ND 4.0.
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Introduction

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The recent COVID-19 pandemic has raised awareness to the lack of scientific understanding surrounding human respiratory aerosol generation and disease transmission. Activities like sneezing, coughing, talking, and breathing produce respiratory aerosols–airborne mucosalivary particles with aerodynamic diameters less than 100 μm that emanate from the respiratory tract. Respiratory aerosols can carry pathogens and therefore play a key role in infectious disease transmission. (1−5)
Evidence suggests that the SARS-CoV-2 virus (which causes COVID-19) is transmitted via infectious respiratory aerosols (6−8) and at distances beyond 1.8 m (6 ft). (9) Strategies to reduce exposure risk are predicated on understanding the rate and variability of respiratory aerosol emissions as a function of various human activities. Without such information, public health authorities must make highly uncertain recommendations, which can result in the precautionary principle dictating policy. (10)
For example, a COVID-19 outbreak in March 2020 occurred among singers in Skagit County, Washington, USA, in which 53 of the 61 individuals who attended a single choir practice became infected, three were hospitalized, and two died. (11) As a result of this and similar outbreaks, along with mounting uncertainties about modes of transmission, the performing arts industry effectively shut down in 2020. Several studies have characterized aerosol emissions from nonvocal (i.e., breathing) and vocalization activities, (12−16) with volume playing a major role in emissions, (16) and activities like singing generating more aerosol than talking. (12,14,15,17) These studies, however, have generally been small in size and limited in scope, which limits population generalizability.
Therefore, we sought to characterize respiratory aerosol emissions from a large panel of individuals of varying age (and sex) while talking and singing, in a laboratory setting, to help elucidate the relative risks and potential for infection transmission from these activities. We modeled these emissions as a function of participant volume (sound pressure level) and exhaled CO2, as the latter has been proposed as an indicator of infection risk indoors. (18)

Materials and Methods

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Healthy volunteer performers aged 12 years and older were recruited to participate in the study (CSU IRB Approval, 20-10174H). Participants from four performance specialties were recruited (vocalists, actors, dancers, and woodwind/brass instrumentalists). Recruitment was targeted to provide equal blocks of minors (ages 12 to 18) and adults (ages 18 and older). Participants self-reported their demographic information, including age, height, weight, race, ethnicity, and sex assigned at birth via questionnaire.
Each participant completed a series of maneuvers specific to their specialty and ability level, along with two generic vocal maneuvers: talking and singing. This paper is focused on respiratory aerosol emissions from the generic vocal maneuvers, which were singing “Happy Birthday” and reading “The Caterpillar” (19) out loud at a conversational volume and style. The reading passage was chosen because it included a broad range of sound classes and articulations, while maintaining simple syntax. Each vocal maneuver was repeated continuously over a period of 4 min, during which a single, time-averaged measurement was recorded.
Participants undertook the maneuvers inside a 3.45 m × 2.8 m × 2.45 m environmental chamber (20) ventilated with HEPA-filtered air. Chamber airflow was controlled, and environmental conditions (temperature, humidity) inside the chamber were logged along with all measurement data using LabVIEW (National Instruments, TX) instrument control and data acquisition software. A constant-volume sampling apparatus (10 L·min–1) was used to capture aerosol emissions from participants (Figure S2). To establish background particle levels, participants were asked to sit in a corner of the chamber, approximately 1.5 m from the sampling apparatus, while wearing a face mask.
Respiratory aerosol concentrations (between 0.25 and 33 μm) were measured by an optical particle counter (OPC; Model 11-D, Grimm Aerosol Technik, Germany) and are reported in terms of raw concentrations (i.e., particle concentration measured downstream of the participant within the sampling apparatus [particles·L–1 of sampled air]) and also in terms of exhaled concentrations (i.e., raw concentration scaled by the measured CO2 mixing ratio and standardized to estimate particles·L–1 of exhaled breath). Details of these calculations and related background corrections are provided in the Supporting Information.
We fit a linear model (Model 1) to explore the effect of maneuver, sex, and age on the raw respiratory aerosol concentrations. Maneuver (talking vs singing), sex (male vs female), and age (minor vs adult) were binary variables in the model

Model 1

(1)
where βi represents the effect estimate for each predictor in the model. We also fit a linear model (Model 2) to explore the effect of maneuver, sex, age, and sound pressure level on exhaled concentrations of respiratory aerosols

Model 2

(2)
where sound pressure (dBA) represents the A-weighted and z-scaled sound pressure level measured above each participant. (21) All data analyses were conducted in R (R Core Team, version 4.0.4).

Results and Discussion

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Emissions from vocal maneuvers were successfully collected from 63 participants. Forty-six percent of participants (n = 29) were female, defined as sex assigned at birth. Participants ranged from 12 to 61 years old (mean age 23 years); 27 participants were adults (age ≥18). Of the 63 participants, 11% (n = 7) had specific vocal training (acting and/or singing), while the remaining 89% (n = 56) were trained as instrumentalists.
Table 1 provides summary statistics for measured sound pressure, CO2 mixing ratio, and respiratory aerosol concentrations by maneuver and demographics. The volume, CO2, and raw concentration data reported here are endogenous to the measurement system (i.e., their magnitude depends on the experimental setup), whereas the exhaled concentration data are exogenous and, thus, more appropriate for generalization beyond the conditions reported here. However, because we sampled near-field emissions at a flow rate that approximates average human breathing (10 L·min–1), the raw concentration data reported here are likely to be representative of concentrations encountered in the near-field of human vocalization. Further, the relative differences between our observed variables (i.e., raw emissions by maneuver, age, and sex) are real, and those differences are unlikely to be affected by the system of measurement. We calculate respiratory aerosol emission rates (#·s–1) assuming a minute ventilation rate of 7.5 L·min–1.
Table 1. Descriptive Statistics for Sound Pressure, Carbon Dioxide (CO2), and Respiratory Aerosol Emissions as a Function of Maneuver and Participant Demographics
 talkingsingingadultsminorsmalefemale
 mean (min, max)IQRmean (min, max)IQRmean (min, max)IQRmean (min, max)IQRmean (min, max)IQRmean (min, max)IQR
sound pressure (dBA)67.5 (62.4, 73)3.970.0 (61.8, 77)6.270.3 (62.9, 77)5.3667.2 (61.8, 73.9)4.5967.3 (61.8, 77)3.270.0 (62.8, 77)4.32
CO2 (ppmV in air)3797 (573, 10125)21434122 (635, 9790)24494697 (635,10125)23273411 (573, 7087)23473310 (573, 5692)20554471 (641, 10125)2585
raw concn (#·L-1 air)142 (<50a, 547)144278 (<50, 937)233270 (<50, 937)274161 (<50, 727)108235 (<50, 848)111173 (<50, 937)205
exhaled concn (#·L-1 breath)1915 (<450a, 7269)15823289 (<450, 9551)23592825 (<450, 9551)21772390 (<450, 7297)16962515 (<450, 9551)16732646 (<450, 8253)1903
exhaled rate (#·s-1)239 (<56a, 909)198411 (<56, 1194)295353 (<56, 1194)272299 (<56, 912)212314 (<56, 1194)209331 (<56, 1032)238
a

Raw concentrations <50 particles·L–1 of air, exhaled concentrations <450 particles·L–1 of breath, and exhaled rates <56 particles·s–1 indicate concentrations below the method detection limits. All data are background corrected. IQR: interquartile range. The exhaled rate was calculated from exhaled concentration and assuming a minute ventilation rate of 7.5 L·min–1.

Raw Concentration Emissions

Shown in Figure 1 are raw concentration measurements as a function of age, sex, and maneuver. Despite the wide variation in respiratory aerosol emission rates within categories, important differences exist. In single-variable models, being adult, male, and singing (vs talking) produced a significant increase in raw respiratory aerosol emissions. In the multivariate model (Model 1 and Table S1), singing resulted in an increase of 133 particles·L–1 (95% CI: 73, 194) vs talking, being male resulted in an increase of 59 particles·L–1 (95% CI: 0, 120) vs being female, and being a minor resulted in a decrease of emissions of 107 particles·L–1 (95% CI: −168, −46) in the air immediately downstream of a participant. Alsved et al. (12) also measured aerosol emissions from 12 adults who were either professional or amateur trained singers; they reported that singing generated more particles than talking and that volume played a role in emissions. Our respiratory aerosol emissions agree with Johnson et al., who estimated a (raw) particle number concentration of 154·L–1 from talking (vs our study average estimate of 142·L–1; Table 1). Assuming a human breathing rate of 7.5 L·min–1, our mean emission rates for exhaled aerosol equate to 239 (range: 56–909) and 411 (56–1194) particles·s–1 from talking and singing, respectively (Table 1). These values overlap with those of Alsved et al., (12) who reported 270 (120–1380) and 690 (320–2870) particles·s–1 for adult talking and singing, respectively, and Mürbe et al. (14) who reported a range from 320 to 2870 particles·s–1 for adult singing. The aerosol size distribution reported by the OPC (Figure S7) was log-normal and bimodal, with one mode centered at 0.5 μm and the other at 1.3 μm, in approximate agreement for the breathing and laryngeal emission modes reported by Johnson et al. (22)

Figure 1

Figure 1. Raw respiratory aerosol concentrations (0.25–33 μm in diameter) measured across different populations and activities. Solid line represents the median, boxes delineate the IQR, and whiskers represent IQR·1.5 or the data minima. The horizontal, dashed line represents a method quantification limit of 50 particles·L–1. Participant data are shown as open circles.

To investigate potential physiologic mechanisms behind the age and sex differences, we examined correlations between raw concentration emissions first with voice volume and then with exhaled CO2. Asadi et al. (16) demonstrated that voice volume was correlated with respiratory aerosol emissions from speech, which we confirm among our panel of 63 volunteers (r2 = 0.37; Figure S2). Increased sound pressure from vocalization is a result of more energy imparted to the vocal folds (in terms of vibrational amplitude), and this increase in vibrational energy from subglottal pressure (23) is likely the putative mechanism for increased probability of droplet release from the vocal folds. (22) Similar to Asadi et al., the strength of the correlation in Figure S4 is diminished due to the large person-to-person variability in base aerosol emissions from vocalization. This between-person variability is also evident in Figure 1.
We also observed a statistically significant correlation between raw concentrations and exhaled CO2 (r2 = 0.3; Figure S5), which we hypothesize is due to increased lung capacity (and hence, increased expiration) in both males and adults. These relationships are confirmed in Table S2, where being male, adult, and vocalizing at higher volume are all significantly associated with increases in exhaled CO2. The results are consistent with adults and males having larger vital lung capacity (i.e., the amount of air that can be forced out of the lungs in a single breath), on average, than minors and females (24,25) and, therefore, higher CO2 emission rates. We hypothesize that increased vital capacity presents more opportunity for the release of “fluid film burst” droplets (26,27) from the respiratory bronchioles upon exhalation (though this hypothesis remains untested). We note that vocal ability (defined as having professional vocal arts experience, n = 7/63 participants) was not associated with either CO2 or respiratory aerosol emissions, although our sample for this test was small (data not shown).

Exhaled Concentrations

Given that exhaled CO2 was associated with raw respiratory aerosol concentrations, we normalized each participant’s raw concentrations to their average CO2 emissions during a maneuver and then scaled these values to represent emissions per liter of exhaled breath (concentrationexhaled; shown in Figure S6). We then evaluated whether the effects of participant age and sex (both as binary variables) persisted in a linear model (Model 2) that included voice volume.
The results of this model, shown as percent increase relative to a reference case (or by one standard deviation increase in the case of sound pressure level), are provided in Figure 2 and Table S3. Interestingly, the sex and age effects on exhaled respiratory aerosol concentrations are no longer statistically significant (Figure 2) in a model that accounts for voice volume and CO2 emissions, but the effect of singing (vs talking) persists. Singing produced an estimated 64% (95% CI: 28–114%) more particles on a per-breath (or per-CO2 basis) than talking, while each ∼5 dBA increase in volume produced 50% more particles than the reference level of 68 dbA. Our interpretation of these results is that voice loudness and CO2 emission rates are the causal factors influencing our model results. That is, males (and adults) tend to vocalize at higher volumes and emit more CO2 than females (and children), and these behavioral/physiological factors explain most of the age and sex differences in raw aerosol emissions that are shown in Figure 1.

Figure 2

Figure 2. Modeled change (% difference) in exhaled concentrations when accounting for maneuver type, loudness, age, and sex. Error bars represent 95% confidence intervals. Data for particles from 0.25 to 33 μm in diameter.

These findings imply that indoor monitoring of ambient CO2 levels should account for age and sex differences (in raw respiratory aerosol emissions) and, thus, provide a relatively robust estimator for aerosol-based infection risk, as suggested by Peng. (18) However, our results also suggest that concurrent monitoring of indoor volume levels is warranted, as the volume effect persisted in a model that accounted for exhaled CO2. Further, control of infection risk must also consider room occupancy levels and whether singing or talking is taking place indoors (or neither), as singing produces increased emissions, even after accounting for voice loudness and exhaled CO2.

Strengths, Limitations, and Future Work

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Our study was conducted on a large sample of participants with a wide range of ages, both sexes, and diverse physiology (e.g., varied body sizes), which allows us to draw conclusions about average population-level emissions that are more generalizable to the public than previous studies. The importance of achieving a larger sample size is emphasized by the fact that our study was able to identify several “outlier” individuals, whose emissions varied drastically from others. For example, the highest emitting singer from our sample produced concentrations that were more than 500% higher than the median value (and over 42 times greater than the lowest emitting singer in our sample, both for raw and exhaled emissions). These results support hypotheses that some individuals within a population may be “superspreaders” of infectious respiratory diseases due to individual variations in physiology, in addition to aspects of virology and behavioral patterns that result in more contacts and transmission for certain people compared to others. (18,28−30) More work is needed to determine whether those individuals who produce higher total respiratory aerosol emissions are also capable of emitting higher pathogenic counts (e.g., viral shedding loads), though evidence suggests that SARS-CoV-2 infection produces increased respiratory aerosol emissions. (31)
We conducted our study in a laboratory environment, wherein participants were asked to perform controlled, predetermined vocal maneuvers on command, rather than measuring emissions in a real-world setting. A major benefit of the controlled experimental design is that we could reduce background (ambient air) particle levels through chamber air filtration, which enabled us to better isolate and measure respiratory aerosols from vocalization. Respiratory aerosol emissions occur at levels below typical ambient air concentrations. Reduction of background is critical for making measurements that allow us to statistically differentiate respiratory aerosol levels across activities or demographic factors. However, controlled designs may lack generalizability to real-world situations, as the controlled protocols may be inherently different or lack variability seen in the real world. We attempted to counter this concern by choosing a talking maneuver that was designed to include a range of sounds and speech patterns representative of standard English-language conversations. (19) Similarly, we chose a song for the singing maneuver that was common and basic.
Our study was limited to ages 12 to 64 years old. Whether the relationship we observed between emissions, voice volume, and exhaled CO2 holds for young children, as well as for older adults, is unknown (though our model results are consistent when stratified to include only minors or only adult age groups; data not shown). Additional research is needed to characterize respiratory aerosol emissions during early childhood development. Similarly, while we measured emissions over a range of voice loudness by asking participants to talk and sing, we did not consider other types of vocal activities like shouting or whispering. Our results indicate that voice loudness is an explanatory factor for differences in emissions from talking to singing, and therefore one would hypothesize that shouting (at a higher volume) or whispering (lower volume) would produce more and less emissions, respectively. However, these activities were not evaluated here.
We did not quantify respiratory disease transmission risk. For safety purposes, we limited participants to healthy individuals who were not experiencing any symptoms of COVID-19 (or other respiratory illness) and had no known recent exposure to someone who was sick with COVID-19. The amount of respiratory aerosol generated by an individual is but one of several factors that play a role in transmission. Factors such as the pathogen’s infectious dose, viral loads in the source individual, and susceptibility of the host (which may be impacted by individual’s physiology, genetics, and comorbidities), as well as a wide range of environmental and social factors related to human-to-human interactions all impact the likelihood that a disease is transmitted from one individual to another. Our results suggest that measuring volume and exhaled CO2 may be useful proxies for respiratory aerosol emissions indoors. However, further work is needed to understand how these proxies correlate to risk. For example, although people naturally tend to vocalize at a loudness levels above ambient background, it is unclear whether monitoring the loudness of specific vocal frequencies or total noise (i.e., average A-weighted decibels) would be needed. Additionally, while our work suggests that exhaled CO2 and voice volume have potential for understanding average population emissions, neither of these metrics explains the “superemission” phenomenon, which was evident among at least one of our participants. There is large interindividual variation in respiratory aerosol emissions. Future work to validate CO2 and loudness as indicators of infection risk should also investigate, within real-world built environment settings, how nonvocal respiratory emissions (i.e., breathing), nonhuman sources of CO2 (e.g., combustion for cooking, room heating, and cooling) and noise, and aerosol sinks such as in-room filtration interact and influence these metrics across space and time.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.estlett.1c00760.

  • Details on data handling and statistical analyses and results of histogram of participants by age (Figure S1), description of testing facility and sampling schematic (Figure S2), signal-to-noise ratios for raw concentration data (Figure S3), correlation plots between respiratory aerosol emissions and participant voice volume (Figure S4) and measured CO2 mixing ratios (Figure S5), exhaled respiratory aerosol concentrations (Figure S6), plot of measured aerosol size distribution from singing (Figure S7), and outputs from linear models (Tables S1–S3) (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • John Volckens - Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United StatesMechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United StatesOrcidhttps://orcid.org/0000-0002-7563-9525 Email: [email protected]
  • Authors
    • Nicholas Good - Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
    • Kristen M. Fedak - Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
    • Dan Goble - School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United States
    • Amy Keisling - School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United StatesMechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
    • Christian L’Orange - Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
    • Emily Morton - School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United States
    • Rebecca Phillips - School of Music, Theatre, and Dance, Colorado State University, Fort Collins, Colorado 80523, United States
    • Ky Tanner - Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work was funded by unrestricted philanthropic donations to the School of Music, Theatre, and Dance at Colorado State University. The authors wish to acknowledge input into the experimental design from members of the study scientific advisory board, including Allen Henderson (Georgia Southern University), Charles Henry (Colorado State University), Emily Morgan (Colorado State University), Heather Pidcoke (Colorado State University), and Timothy Rhea (Texas A&M University).

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    Fedak, K. M.; Good, N.; Walker, E. S.; Balmes, J.; Brook, R. D.; Clark, M. L.; Cole-Hunter, T.; Devlin, R.; L’Orange, C.; Luckasen, G. Acute changes in lung function following controlled exposure to cookstove air pollution in the subclinical tests of volunteers exposed to smoke (STOVES) study. Inhalation Toxicol. 2020, 32 (3), 115123,  DOI: 10.1080/08958378.2020.1751750
  21. 21
    Montgomery, D. C.; Peck, E. A.; Vining, G. G. Introduction to linear regression analysis, 6th ed.; John Wiley & Sons: Hoboken, NJ, 2021.
  22. 22
    Johnson, G.; Morawska, L.; Ristovski, Z.; Hargreaves, M.; Mengersen, K.; Chao, C. Y. H.; Wan, M.; Li, Y.; Xie, X.; Katoshevski, D. Modality of human expired aerosol size distributions. J. Aerosol Sci. 2011, 42 (12), 839851,  DOI: 10.1016/j.jaerosci.2011.07.009
  23. 23
    Gramming, P.; Sundberg, J.; Ternström, S.; Leanderson, R.; Perkins, W. H. Relationship between changes in voice pitch and loudness. Journal of voice 1988, 2 (2), 118126,  DOI: 10.1016/S0892-1997(88)80067-5
  24. 24
    Dockery, D. W.; Ware, J. H.; Ferris, B. G., Jr; Glicksberg, D. S.; Fay, M. E.; Spiro, A., III; Speizer, F. E. Distribution of forced expiratory volume in one second and forced vital capacity in healthy, white, adult never-smokers in six US cities. Am. Rev. Respir. Dis. 1985, 131 (4), 511520,  DOI: 10.1164/arrd.1985.131.4.511
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    Todisco, T.; Grassi, V.; Dottorini, M.; Sorbini, C. Reference values for flow-volume curves during forced vital capacity breathing in male children and young adults. Respiration 2004, 39 (1), 17,  DOI: 10.1159/000194191
  26. 26
    Holmgren, H.; Ljungström, E. Influence of film dimensions on film droplet formation. J. Aerosol Med. Pulm. Drug Delivery 2012, 25 (1), 4753,  DOI: 10.1089/jamp.2011.0892
  27. 27
    Johnson, G. R.; Morawska, L. The mechanism of breath aerosol formation. J. Aerosol Med. Pulm. Drug Delivery 2009, 22 (3), 229237,  DOI: 10.1089/jamp.2008.0720
  28. 28
    Galvani, A. P.; May, R. M. Dimensions of superspreading. Nature 2005, 438 (7066), 293295,  DOI: 10.1038/438293a
  29. 29
    Lloyd-Smith, J. O.; Schreiber, S. J.; Kopp, P. E.; Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 2005, 438 (7066), 355359,  DOI: 10.1038/nature04153
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    Coleman, K. K.; Tay, D. J. W.; Sen Tan, K.; Ong, S. W. X.; Son, T. T.; Koh, M. H.; Chin, Y. Q.; Nasir, H.; Mak, T. M.; Chu, J. J. H.; Milton, D. K.; Chow, V. T. K.; Tambyah, P. A.; Chen, M.; Wai, T. K. Viral Load of SARS-CoV-2 in Respiratory Aerosols Emitted by COVID-19 Patients while Breathing, Talking, and Singing. Clin. Infect. Dis. 2021,  DOI: 10.1093/cid/ciab691
  31. 31
    Group, f. t. U. o. M. S. R. Infectious SARS-CoV-2 in Exhaled Aerosols and Efficacy of Masks During Early Mild Infection. Clin. Infect. Dis. 2021,  DOI: 10.1093/cid/ciab797

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  • Abstract

    Figure 1

    Figure 1. Raw respiratory aerosol concentrations (0.25–33 μm in diameter) measured across different populations and activities. Solid line represents the median, boxes delineate the IQR, and whiskers represent IQR·1.5 or the data minima. The horizontal, dashed line represents a method quantification limit of 50 particles·L–1. Participant data are shown as open circles.

    Figure 2

    Figure 2. Modeled change (% difference) in exhaled concentrations when accounting for maneuver type, loudness, age, and sex. Error bars represent 95% confidence intervals. Data for particles from 0.25 to 33 μm in diameter.

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      Gramming, P.; Sundberg, J.; Ternström, S.; Leanderson, R.; Perkins, W. H. Relationship between changes in voice pitch and loudness. Journal of voice 1988, 2 (2), 118126,  DOI: 10.1016/S0892-1997(88)80067-5
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      Group, f. t. U. o. M. S. R. Infectious SARS-CoV-2 in Exhaled Aerosols and Efficacy of Masks During Early Mild Infection. Clin. Infect. Dis. 2021,  DOI: 10.1093/cid/ciab797
  • Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.estlett.1c00760.

    • Details on data handling and statistical analyses and results of histogram of participants by age (Figure S1), description of testing facility and sampling schematic (Figure S2), signal-to-noise ratios for raw concentration data (Figure S3), correlation plots between respiratory aerosol emissions and participant voice volume (Figure S4) and measured CO2 mixing ratios (Figure S5), exhaled respiratory aerosol concentrations (Figure S6), plot of measured aerosol size distribution from singing (Figure S7), and outputs from linear models (Tables S1–S3) (PDF)


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